Use a table pipeline as a reusable function

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Convert a table with computed columns into a callable function for multi-agent workflows and pipeline composition.

Problem

You have a table that runs a complex pipeline—LLM calls, tool use, post-processing—and you want to reuse that entire pipeline from other tables. Copy-pasting computed column definitions is error-prone and hard to maintain.

Solution

What’s in this recipe:

Create an “agent” table with computed columns
Convert the table to a callable UDF with pxt.udf(table, return_value=...)
Use the table UDF in other tables’ computed columns

You wrap an entire table pipeline as a function. When you call this function from another table, it inserts a row into the agent table, runs all computed columns, and returns the specified output column.

Setup

%pip install -qU pixeltable openai

import getpass
import os

if 'OPENAI_API_KEY' not in os.environ:
    os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key: ')

import pixeltable as pxt
from pixeltable.functions.openai import chat_completions

# Create a fresh directory
pxt.drop_dir('table_udf_demo', force=True)
pxt.create_dir('table_udf_demo')

Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory ‘table_udf_demo’.
<pixeltable.catalog.dir.Dir at 0x17fd6e9d0>

Create an agent table with computed columns

You create a table that encapsulates a complete pipeline. This example builds a summarization agent:

# Create the agent table with input column
summarizer = pxt.create_table(
    'table_udf_demo/summarizer', {'text': pxt.String}
)

Created table ‘summarizer’.

# Add the LLM call as a computed column
summarizer.add_computed_column(
    response=chat_completions(
        messages=[
            {
                'role': 'user',
                'content': 'Summarize this in one sentence:\n\n'
                + summarizer.text,
            }
        ],
        model='gpt-4o-mini',
    )
)

Added 0 column values with 0 errors.
No rows affected.

# Extract the summary text
summarizer.add_computed_column(
    summary=summarizer.response.choices[0].message.content
)

Added 0 column values with 0 errors.
No rows affected.

Convert the table to a UDF

You use pxt.udf(table, return_value=...) to convert the table into a callable function. The return_value specifies which column to return:

# Convert the summarizer table into a callable UDF
summarize = pxt.udf(summarizer, return_value=summarizer.summary)

Use the table UDF in another table

You can now use summarize() as a computed column in any other table:

# Create a table that uses the summarizer
articles = pxt.create_table(
    'table_udf_demo/articles',
    {'title': pxt.String, 'content': pxt.String},
)

Created table ‘articles’.

# Add the table UDF as a computed column
articles.add_computed_column(summary=summarize(text=articles.content))

Added 0 column values with 0 errors.
No rows affected.

# Insert articles - summaries are generated automatically
articles.insert(
    [
        {
            'title': 'Climate Report',
            'content': 'Global temperatures rose by 1.2 degrees Celsius above pre-industrial levels last year, marking the hottest year on record. Scientists attribute this to continued greenhouse gas emissions and a strong El Nino pattern. The report calls for immediate action to reduce carbon emissions.',
        },
        {
            'title': 'Tech Merger',
            'content': 'Two major semiconductor companies announced a merger valued at $50 billion. The combined entity will control 30% of the global chip market. Regulators in multiple countries will review the deal over the next 18 months.',
        },
    ]
)

Inserting rows into `articles`: 2 rows [00:00, 196.58 rows/s]
Inserted 2 rows with 0 errors.
2 rows inserted, 6 values computed.

# View results
articles.select(articles.title, articles.summary).collect()

Explanation

How table UDFs work:

Consumer table row → Table UDF called → Agent table inserts row →
Computed columns run → Return value extracted → Consumer gets result

When to use table UDFs vs @pxt.query:

Key benefits:

Encapsulation: Hide complex pipeline details behind a simple function call
Reusability: Use the same agent from multiple consumer tables
Persistence: All intermediate results are stored in the agent table for debugging
Composition: Chain agents together for multi-stage workflows

Welcome to Pixeltable

Tools

Core Concepts

How-To

Use a table pipeline as a reusable function

Problem

Solution

Setup

Create an agent table with computed columns

Convert the table to a UDF

Use the table UDF in another table

Explanation

See also

​Problem

​Solution

​Setup

​Create an agent table with computed columns

​Convert the table to a UDF

​Use the table UDF in another table

​Explanation

​See also

Problem

Solution

Setup

Create an agent table with computed columns

Convert the table to a UDF

Use the table UDF in another table

Explanation

See also